PATCH DYNAMICS OF MUSSELS ON ROCKY SHORES: INTEGRATING PROCESS TO UNDERSTAND PATTERN
Bibliographic record
Abstract
Within any particular landscape, patch structure and dynamics likely will differ among habitat types because of environmental differences influencing the intensity and outcome of biological interactions. We compared the dynamics of natural and experimentally constructed mussel patches (Mytilus trossulus and M. edulis) in two intertidal habitats, tidepools and emergent rock, over a time series (5, 10, 15 mo) and among seasons (three successive 5-mo intervals). In tidepools, mussels naturally occurred in small patches (median <25 cm2). In contrast, mussels on emergent rock formed extensive beds with decimeter-scale gaps, but these beds start as small patches following disturbance such as ice scour. For our experimental patches (15 cm2) in both habitats, we assessed the relative importance of physical (wave disturbance) and biological (predation, growth, recruitment, and immigration) processes in determining patch size and structure. Although individual experimental and natural patches varied greatly in size over time, mean patch area remained relatively constant. In general, mean size of individuals in experimental patches decreased due to the loss of larger mussels while numbers increased due to recruitment. Wave disturbance, which removes and redistributes mussels, appeared to be more important than predation (by the whelk Nucella lapillus) in determining patch structure and dynamics on this shore, although losses due to either process did not differ consistently between habitats over time. Individual growth rates were low (shell length ≤0.4 mm/mo) but greater in tidepools than on emergent rock, whereas recruitment and immigration rates generally did not differ between habitats. Although each process contributed to changes in patch size and structure, collectively they did not result in a marked divergence in mean patch area or biomass between tidepools and emergent rock over the 15-mo experiment. We suggest that the broad patterns of distribution and abundance of mussels develop slowly on this shore largely because of low individual growth rates. Over time scales of years, however, positive relationships between individual growth and recruitment rates and patch size may accelerate the expansion of aggregations on emergent rock. Mussel assemblages at our site differ from those examined in many other studies in terms of the relative importance of wave dislodgment and predation as causes of loss and in the role of growth in offsetting these losses. This study shows how integrative approaches, with monitoring of patches and individuals within patches, can provide detailed mechanistic understanding of patch structure and dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".